Structural Design for Enhanced Noise Performance Using Genetic Algorithm and Other Optimization Techniques

  • A. J. Keane
Conference paper


The control of structural vibration in aeroplanes and ships is of great importance in achieving low noise targets. Currently, such control is effected using viscoelastic coating materials although much current research is concerned with active, anti-noise based control measures. Recent studies using Genetic Algorithm (GA) optimization methods in the field of Statistical Energy Analysis (SEA) suggest that it may be possible to design passive noise filtration characteristics into such structures. This paper reports initial work in this field: it compares GA’s with more classical optimization methods and shows how improvements in noise performance can be obtained for the simple structures considered.


Genetic Algorithm Mode Shape Genetic Algorithm Parameter Mass Profile Reduce Population Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    A. J. Keane and C. S. Manohar, “Tower Flow Variability in a Pair of Coupled Stochastic Rods,” J. Sound Vib. 164(2) (1993).Google Scholar
  2. 2.
    H. G. Davies, “Random vibration of distributed systems strongly coupled at discrete points,” J. Acoust. Soc. Am. 54(2) pp. 507–515 (1973).CrossRefGoogle Scholar
  3. 3.
    P. J. Remington and J. E. Manning, “Comparison of Statistical Energy Analysis power flow predictions with an ‘exact’ calculation,” J. Acoust. Soc. Am. 57(2) pp. 374–379 (1975).CrossRefGoogle Scholar
  4. 4.
    A. J. Keane and W. G. Price, “A Note on the Power Rowing between Two Conservatively Coupled Multi-Modal Sub-systems,”, J. Sound Vib. 144(2) pp. 185–196 (1991).CrossRefGoogle Scholar
  5. 5.
    J. N. Siddall, Optimal Engineering Design: Principles and Applications, Marcel Dekker, Inc., New York (1982).Google Scholar
  6. 6.
    NAg E04UCF, NAg Mark 12 Reference Manual, Numerical Algorithms Group Ltd., Oxford (1987).Google Scholar
  7. 7.
    D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading (1989).Google Scholar
  8. 8.
    S. Kirkpatrick, C.D. Gelatt, Jr., and M.P. Vecchi, “Optimization by simulated annealing,” Science 220(4598) pp. 671–680 (May 1983).MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag/Wien 1993

Authors and Affiliations

  • A. J. Keane
    • 1
  1. 1.Department of Engineering ScienceUniversity of OxfordOxfordUK

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